Description: Algorithmic bias refers to the occurrence of systematic errors in algorithmic decision-making. This phenomenon can arise from various sources, including the data used to train models, the assumptions inherent in the algorithm’s design, and programming decisions. When an algorithm exhibits bias, it can lead to unfair or discriminatory outcomes, negatively affecting specific groups of people. For instance, a hiring algorithm based on historical data may perpetuate gender or racial inequalities if that data reflects past discriminatory practices. The relevance of algorithmic bias is critical in a world where artificial intelligence (AI) is used in numerous applications, including hiring, criminal justice, and healthcare. Neglecting this issue can result in decisions that are not only ineffective but also ethically questionable. Therefore, it is essential for developers and organizations to be aware of potential biases and actively work to mitigate them, ensuring that AI systems are fair and equitable for all users.
